Freelance Data Engineer (Security)
Posted3 hours ago
Experience Without Limits
Come shape the future of brand experience.
At Jack Morton, we design and build head-turning, smile-inducing, impact-driving brand experiences that redefine what experiential marketing can achieve. We help our clients unlock the full value of experiences by turning bold ideas into meaningful connections.
That takes a team that's bold, curious, and acts as one. We build on each other's ideas, challenge conventions, and show up for one another every day. Here, you'll do your best work, grow quickly, and partner across clients, disciplines, and global teams to bring new ideas to life.
This is a place where curiosity drives what’s next, boldness raises the bar, and every win is shared. Where ownership is expected, egos are left at the door, and the work reflects the passion and talent of the people behind it.
Experience without limits—in your work, your growth, and your impact.
Freelance Data Engineer (Security)
Jack IQ Programme ·
Role details
Job title | Data Engineer (Security)
Reports to | Product Lead / Technology Executive
Contract type Freelance (to perm possible)
Duration ASAP – April 27
Location / working pattern - Remote
Start date ASAP
Purpose of the role
Jack Morton is seeking a Data Engineer to help build out the data spine for an AI-enabled event intelligence platform. The role will focus on creating secure and scalable processes to connect operational systems, unstructured project documentation and data from third-party platforms into existing data warehousing infrastructure.
The successful candidate will help transform fragmented agency knowledge into governed, searchable, AI-ready assets while ensuring adherence to privacy, retention, and client-specific data requirements.
Key responsibilities
Create a structured data layer to integrate and model structured data from API enabled sources such as CRM systems, Azure databases, event attendance systems, survey platforms, and financial warehouses.
Create ingestion pipelines for the unstructured knowledge layer that includes Powerpoint presentations, Word documents and unstructured Excel spreadsheets.
Build an AI-Ready document infrastructure that can classify documents, handle versioning, track provenance, and manage metadata, auditability and permissioning.
Implement frameworks for strictest confidentiality and privacy protections, including access controls and security roles, consent tracking, data retention and data deletion management.
Work with members of the Jack Morton team to clearly communicate limitations, timeline expectations, out-of-pocket cost expectations and repercussions to platform design.
Participate in planning discussions on platform roadmap with Jack Morton team members.
Data engineering requirements
Pipeline design, ingestion, schema, data quality, orchestration, tooling.
This role requires strong experience in the following:
Python
SQL
Data Modeling
Data Warehousing (Snowflake experience is desirable)
ETL or ELT development
API Integrations
Batch and event-driven pipelines.
The applicant should understand:
Metadata extraction
OCR workflows
Semantic indexing
Vector databases
Retrieval-Augmented Generation (RAG)
Chunking strategies
Document classification
Embedding pipelines
Security requirements
Access control, tenant isolation, key management, pseudonymisation, audit and logging, compliance frameworks.
Experience with the following is essential, with AI-specific experience being a bonus:
PII handling and understanding of GDPR implications
Data pseudonymization
Data lineage
Retention policies
Audit logging
Data access controls
Platform and tooling
Snowflake, AWS, Bedrock, integration patterns, languages, infrastructure as code.
Snowflake
AWS Cognito
AWS S3
AWS Lamda
AWS DynamoDB
Experience required
Azure or Snowflake data warehousing
Document ingestion and RAG experience
Data governance and security
Modern data modeling skills
API integration
Data pipelines for unstructured documentation
Experience explaining technical infrastructure to non-technical teams
Experience preferred
LLM model building
Understanding of event and experiential marketing
Experience working with a project-based business
First 90 days
What success looks like by day 30, 60 and 90.
Within the first 90 days the data engineer should be integrated into the platform team. They should have delivered a proposal for their plan for the data spine and should be beginning to build out the initial processes to bring it to life.
We make our careers website accessible to any and all users. If you need an accommodation to participate in the application process, please contact us at
Come shape the future of brand experience.
At Jack Morton, we design and build head-turning, smile-inducing, impact-driving brand experiences that redefine what experiential marketing can achieve. We help our clients unlock the full value of experiences by turning bold ideas into meaningful connections.
That takes a team that's bold, curious, and acts as one. We build on each other's ideas, challenge conventions, and show up for one another every day. Here, you'll do your best work, grow quickly, and partner across clients, disciplines, and global teams to bring new ideas to life.
This is a place where curiosity drives what’s next, boldness raises the bar, and every win is shared. Where ownership is expected, egos are left at the door, and the work reflects the passion and talent of the people behind it.
Experience without limits—in your work, your growth, and your impact.
Freelance Data Engineer (Security)
Jack IQ Programme ·
Role details
Job title | Data Engineer (Security)
Reports to | Product Lead / Technology Executive
Contract type Freelance (to perm possible)
Duration ASAP – April 27
Location / working pattern - Remote
Start date ASAP
Purpose of the role
Jack Morton is seeking a Data Engineer to help build out the data spine for an AI-enabled event intelligence platform. The role will focus on creating secure and scalable processes to connect operational systems, unstructured project documentation and data from third-party platforms into existing data warehousing infrastructure.
The successful candidate will help transform fragmented agency knowledge into governed, searchable, AI-ready assets while ensuring adherence to privacy, retention, and client-specific data requirements.
Key responsibilities
Create a structured data layer to integrate and model structured data from API enabled sources such as CRM systems, Azure databases, event attendance systems, survey platforms, and financial warehouses.
Create ingestion pipelines for the unstructured knowledge layer that includes Powerpoint presentations, Word documents and unstructured Excel spreadsheets.
Build an AI-Ready document infrastructure that can classify documents, handle versioning, track provenance, and manage metadata, auditability and permissioning.
Implement frameworks for strictest confidentiality and privacy protections, including access controls and security roles, consent tracking, data retention and data deletion management.
Work with members of the Jack Morton team to clearly communicate limitations, timeline expectations, out-of-pocket cost expectations and repercussions to platform design.
Participate in planning discussions on platform roadmap with Jack Morton team members.
Data engineering requirements
Pipeline design, ingestion, schema, data quality, orchestration, tooling.
This role requires strong experience in the following:
Python
SQL
Data Modeling
Data Warehousing (Snowflake experience is desirable)
ETL or ELT development
API Integrations
Batch and event-driven pipelines.
The applicant should understand:
Metadata extraction
OCR workflows
Semantic indexing
Vector databases
Retrieval-Augmented Generation (RAG)
Chunking strategies
Document classification
Embedding pipelines
Security requirements
Access control, tenant isolation, key management, pseudonymisation, audit and logging, compliance frameworks.
Experience with the following is essential, with AI-specific experience being a bonus:
PII handling and understanding of GDPR implications
Data pseudonymization
Data lineage
Retention policies
Audit logging
Data access controls
Platform and tooling
Snowflake, AWS, Bedrock, integration patterns, languages, infrastructure as code.
Snowflake
AWS Cognito
AWS S3
AWS Lamda
AWS DynamoDB
Experience required
Azure or Snowflake data warehousing
Document ingestion and RAG experience
Data governance and security
Modern data modeling skills
API integration
Data pipelines for unstructured documentation
Experience explaining technical infrastructure to non-technical teams
Experience preferred
LLM model building
Understanding of event and experiential marketing
Experience working with a project-based business
First 90 days
What success looks like by day 30, 60 and 90.
Within the first 90 days the data engineer should be integrated into the platform team. They should have delivered a proposal for their plan for the data spine and should be beginning to build out the initial processes to bring it to life.
We make our careers website accessible to any and all users. If you need an accommodation to participate in the application process, please contact us at
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